A rapidly growing financial-services organization is running a multi-year Digital Transformation Program consisting of three concurrent projects: a mobile banking app, an API modernization effort, and a data-analytics platform. Each project has committed to a fixed launch date aligned to a regulatory compliance window. During the monthly steering-committee review, the Program Manager presents a timeline showing that the API modernization work is running 12 days late, the mobile app is 6 days early, and the analytics platform is exactly on schedule. Senior leadership asks whether these schedule differences are random fluctuations that can be expected at this stage or signs that individual projects need additional intervention. Before reallocating resources or approving overtime, the Program Manager decides to perform a statistical study on the last 16 weekly schedule-performance indices collected from all three project teams. The goal is to distinguish normal process variation (common causes) from unusual variation triggered by team-specific issues or external events (special causes). Which statistical quality-management tool will best help the Program Manager make this determination?
A control chart-one of the Seven Basic Quality Tools-plots schedule-performance data against statistically derived upper and lower control limits. Points that remain within the limits and show no rule-breaking pattern represent common-cause variation, meaning the overall delivery process is stable. Points outside the limits or forming trends signal special-cause variation that warrants investigation of a specific project or event. Pareto charts simply rank the relative frequency or impact of causes, scatter diagrams reveal correlations between two variables, and histograms display a snapshot distribution at one point in time. None of these alternatives differentiate common versus special causes over time.
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PgMP
Program Life Cycle Management
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